HyperDn is a configuration-conditioned predictor that transfers oracle supervision across denoising paradigms to achieve near-oracle hyperparameter prediction with few or zero target labels.
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Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.
ICARUS measures flux-averaged differential CCQE-like cross sections in lepton angle, lepton-proton opening angle, and transverse kinematic imbalance variables, finding agreement with event generator predictions within current uncertainties.
citing papers explorer
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Oracle Supervision Transfers for Hyperparameter Prediction in Model-Based Image Denoising
HyperDn is a configuration-conditioned predictor that transfers oracle supervision across denoising paradigms to achieve near-oracle hyperparameter prediction with few or zero target labels.
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Orthogonal reparametrization of the Nelson-Siegel-Svensson interest rate curve model: conditioning, diagnostics, and identifiability
Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.
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Measurement of muon (anti-)neutrino charged-current quasielastic-like cross section using off-axis NuMI beam at ICARUS
ICARUS measures flux-averaged differential CCQE-like cross sections in lepton angle, lepton-proton opening angle, and transverse kinematic imbalance variables, finding agreement with event generator predictions within current uncertainties.